High-contrast and stable operation of a plasmonically-enhanced GSST-based phase-change memory cell using plasmonic chain modes for photonic memory applications
Bibliographic record
Abstract
This study focuses on designing a plasmon-enhanced phase change material (PCM) based optical phase change memory with high reading contrast (≈80%, the best recorded so far) and providing operational stability across different design parameters and read wavelength swings. For this, plasmon chain modes are utilized in tandem with a PCM/Ag nano-antenna with a nanometric cross-section, which acts as a memory element. By adjusting the core and cladding of the waveguide and operating in mono-mode conditions, this high contrast is obtained at 1.04~μm. Furthermore, this high contrast is kept stable by using dipolar transverse plasmon chain modes generated by a series of horizontally placed metallic (silver) nanowires along the propagation axis. For the PCM, GSST ( Ge 2 Sb 2 Se 4 Te 1 ) is considered. Having chain modes interact with PCM, instead of the fundamental TE-like or TM-like mode, shifts the peak wavelength of the transmission spectra of the chain mode, differently for amorphous and crystalline states, hence creating a high contrast near the anti-symmetric cut-off wavelength. We also got a good write (11.5 pJ) and erase (41.4 pJ) energy, write (2 ns) and erase (15 ns) latency values, which are an order of magnitude better than contemporary NAND flash, electrical-PCM, and optical-PCM technologies, and comparable with recent advancements in plasmonic PCM memory schemes. This research is an important step towards implementing photonic neuromorphic computing, which promises even THz speeds of operation, at a reduced power consumption, as high contrast directly relates to high packing/crystallization fractions within the PCM cell, which enables multi-level storage within a single cell.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".